The invention provides a hyper-parameter automatic optimization method and system of non-supervised machine learning. The hyper-parameter automatic optimization method of non-supervised machine learning includes the steps: according to the non-supervised machine learning algorithm, determining an algorithm performance assessment model of the non-supervised machine learning algorithm, the hyper-parameter of the non-supervised machine learning algorithm, the searching space of the hyper-parameter and the evaluation criterion of hyper-parameter optimization; and according to the algorithm performance assessment model, the searching space and the evaluation criterion, determining the optimal value of the hyper-parameter. The hyper-parameter automatic optimization method and system of non-supervised machine learning can deeply analyze the hyper-parameter problem in the non-supervised machine learning algorithm, can analyze the distribution rules of hyper-parameter in the algorithm, can assess the learning effect under different hyper-parameters, and can apply the rules to model training of machine learning so as to achieve the aim of automatically selecting the suitable hyper-parameter,and then the whole optimization process of hyper-parameter is automatically completed, so that the optimization efficiency is high and the usage complexity of the algorithm can be greatly reduced.